"""
Name: BASNet lib
Description: The main module of the neural network.
Source url: https://github.com/NathanUA/BASNet
Modified by Anodev (OPHoperHPO)[https://github.com/OPHoperHPO].
License: MIT License
License:
    Copyright (c) 2019 Xuebin Qin
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.
"""
import torch
import torch.nn as nn
from torchvision import models


## code from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
# __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
#            'resnet152', 'ResNet34P','ResNet50S','ResNet50P','ResNet101P']
#
# resnet18_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet18-5c106cde.pth'
# resnet34_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet34-333f7ec4.pth'
# resnet50_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet50-19c8e357.pth'
# resnet101_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet101-5d3b4d8f.pth'
#
# model_urls = {
#     'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
#     'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
#     'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
#     'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
#     'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
# }

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class BasicBlockDe(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlockDe, self).__init__()

        self.convRes = conv3x3(inplanes, planes, stride)
        self.bnRes = nn.BatchNorm2d(planes)
        self.reluRes = nn.ReLU(inplace=True)

        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = self.convRes(x)
        residual = self.bnRes(residual)
        residual = self.reluRes(residual)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class RefUnet(nn.Module):
    def __init__(self, in_ch, inc_ch):
        super(RefUnet, self).__init__()

        self.conv0 = nn.Conv2d(in_ch, inc_ch, 3, padding=1)

        self.conv1 = nn.Conv2d(inc_ch, 64, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=True)

        self.pool1 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)

        self.pool2 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.relu3 = nn.ReLU(inplace=True)

        self.pool3 = nn.MaxPool2d(2, 2, ceil_mode=True)

        self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn4 = nn.BatchNorm2d(64)
        self.relu4 = nn.ReLU(inplace=True)

        self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)


        self.conv5 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn5 = nn.BatchNorm2d(64)
        self.relu5 = nn.ReLU(inplace=True)


        self.conv_d4 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d4 = nn.BatchNorm2d(64)
        self.relu_d4 = nn.ReLU(inplace=True)

        self.conv_d3 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d3 = nn.BatchNorm2d(64)
        self.relu_d3 = nn.ReLU(inplace=True)

        self.conv_d2 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d2 = nn.BatchNorm2d(64)
        self.relu_d2 = nn.ReLU(inplace=True)

        self.conv_d1 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn_d1 = nn.BatchNorm2d(64)
        self.relu_d1 = nn.ReLU(inplace=True)

        self.conv_d0 = nn.Conv2d(64, 1, 3, padding=1)

        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)

    def forward(self, x):
        hx = x
        hx = self.conv0(hx)

        hx1 = self.relu1(self.bn1(self.conv1(hx)))
        hx = self.pool1(hx1)

        hx2 = self.relu2(self.bn2(self.conv2(hx)))
        hx = self.pool2(hx2)

        hx3 = self.relu3(self.bn3(self.conv3(hx)))
        hx = self.pool3(hx3)

        hx4 = self.relu4(self.bn4(self.conv4(hx)))
        hx = self.pool4(hx4)

        hx5 = self.relu5(self.bn5(self.conv5(hx)))

        hx = self.upscore2(hx5)

        d4 = self.relu_d4(self.bn_d4(self.conv_d4(torch.cat((hx, hx4), 1))))
        hx = self.upscore2(d4)

        d3 = self.relu_d3(self.bn_d3(self.conv_d3(torch.cat((hx, hx3), 1))))
        hx = self.upscore2(d3)

        d2 = self.relu_d2(self.bn_d2(self.conv_d2(torch.cat((hx, hx2), 1))))
        hx = self.upscore2(d2)

        d1 = self.relu_d1(self.bn_d1(self.conv_d1(torch.cat((hx, hx1), 1))))

        residual = self.conv_d0(d1)

        return x + residual


class BASNet(nn.Module):
    def __init__(self, n_channels, n_classes):
        super(BASNet, self).__init__()

        resnet = models.resnet34(pretrained=True)

        # -------------Encoder--------------

        self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1)
        self.inbn = nn.BatchNorm2d(64)
        self.inrelu = nn.ReLU(inplace=True)

        # stage 1
        self.encoder1 = resnet.layer1  # 224
        # stage 2
        self.encoder2 = resnet.layer2  # 112
        # stage 3
        self.encoder3 = resnet.layer3  # 56
        # stage 4
        self.encoder4 = resnet.layer4  # 28

        self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)

        # stage 5
        self.resb5_1 = BasicBlock(512, 512)
        self.resb5_2 = BasicBlock(512, 512)
        self.resb5_3 = BasicBlock(512, 512)  # 14

        self.pool5 = nn.MaxPool2d(2, 2, ceil_mode=True)

        # stage 6
        self.resb6_1 = BasicBlock(512, 512)
        self.resb6_2 = BasicBlock(512, 512)
        self.resb6_3 = BasicBlock(512, 512)  # 7

        # -------------Bridge--------------

        # stage Bridge
        self.convbg_1 = nn.Conv2d(512, 512, 3, dilation=2, padding=2)  # 7
        self.bnbg_1 = nn.BatchNorm2d(512)
        self.relubg_1 = nn.ReLU(inplace=True)
        self.convbg_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bnbg_m = nn.BatchNorm2d(512)
        self.relubg_m = nn.ReLU(inplace=True)
        self.convbg_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bnbg_2 = nn.BatchNorm2d(512)
        self.relubg_2 = nn.ReLU(inplace=True)

        # -------------Decoder--------------

        # stage 6d
        self.conv6d_1 = nn.Conv2d(1024, 512, 3, padding=1)  # 16
        self.bn6d_1 = nn.BatchNorm2d(512)
        self.relu6d_1 = nn.ReLU(inplace=True)

        self.conv6d_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2)  ###
        self.bn6d_m = nn.BatchNorm2d(512)
        self.relu6d_m = nn.ReLU(inplace=True)

        self.conv6d_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2)
        self.bn6d_2 = nn.BatchNorm2d(512)
        self.relu6d_2 = nn.ReLU(inplace=True)

        # stage 5d
        self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1)  # 16
        self.bn5d_1 = nn.BatchNorm2d(512)
        self.relu5d_1 = nn.ReLU(inplace=True)

        self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1)  ###
        self.bn5d_m = nn.BatchNorm2d(512)
        self.relu5d_m = nn.ReLU(inplace=True)

        self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1)
        self.bn5d_2 = nn.BatchNorm2d(512)
        self.relu5d_2 = nn.ReLU(inplace=True)

        # stage 4d
        self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1)  # 32
        self.bn4d_1 = nn.BatchNorm2d(512)
        self.relu4d_1 = nn.ReLU(inplace=True)

        self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1)  ###
        self.bn4d_m = nn.BatchNorm2d(512)
        self.relu4d_m = nn.ReLU(inplace=True)

        self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1)
        self.bn4d_2 = nn.BatchNorm2d(256)
        self.relu4d_2 = nn.ReLU(inplace=True)

        # stage 3d
        self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1)  # 64
        self.bn3d_1 = nn.BatchNorm2d(256)
        self.relu3d_1 = nn.ReLU(inplace=True)

        self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1)  ###
        self.bn3d_m = nn.BatchNorm2d(256)
        self.relu3d_m = nn.ReLU(inplace=True)

        self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1)
        self.bn3d_2 = nn.BatchNorm2d(128)
        self.relu3d_2 = nn.ReLU(inplace=True)

        # stage 2d

        self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1)  # 128
        self.bn2d_1 = nn.BatchNorm2d(128)
        self.relu2d_1 = nn.ReLU(inplace=True)

        self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1)  ###
        self.bn2d_m = nn.BatchNorm2d(128)
        self.relu2d_m = nn.ReLU(inplace=True)

        self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1)
        self.bn2d_2 = nn.BatchNorm2d(64)
        self.relu2d_2 = nn.ReLU(inplace=True)

        # stage 1d
        self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1)  # 256
        self.bn1d_1 = nn.BatchNorm2d(64)
        self.relu1d_1 = nn.ReLU(inplace=True)

        self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1)  ###
        self.bn1d_m = nn.BatchNorm2d(64)
        self.relu1d_m = nn.ReLU(inplace=True)

        self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.bn1d_2 = nn.BatchNorm2d(64)
        self.relu1d_2 = nn.ReLU(inplace=True)

        # -------------Bilinear Upsampling--------------
        self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=False)  ###
        self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear', align_corners=False)
        self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=False)
        self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False)
        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)

        # -------------Side Output--------------
        self.outconvb = nn.Conv2d(512, 1, 3, padding=1)
        self.outconv6 = nn.Conv2d(512, 1, 3, padding=1)
        self.outconv5 = nn.Conv2d(512, 1, 3, padding=1)
        self.outconv4 = nn.Conv2d(256, 1, 3, padding=1)
        self.outconv3 = nn.Conv2d(128, 1, 3, padding=1)
        self.outconv2 = nn.Conv2d(64, 1, 3, padding=1)
        self.outconv1 = nn.Conv2d(64, 1, 3, padding=1)

        # -------------Refine Module-------------
        self.refunet = RefUnet(1, 64)

    def forward(self, x):
        hx = x

        # -------------Encoder-------------
        hx = self.inconv(hx)
        hx = self.inbn(hx)
        hx = self.inrelu(hx)

        h1 = self.encoder1(hx)  # 256
        h2 = self.encoder2(h1)  # 128
        h3 = self.encoder3(h2)  # 64
        h4 = self.encoder4(h3)  # 32

        hx = self.pool4(h4)  # 16

        hx = self.resb5_1(hx)
        hx = self.resb5_2(hx)
        h5 = self.resb5_3(hx)

        hx = self.pool5(h5)  # 8

        hx = self.resb6_1(hx)
        hx = self.resb6_2(hx)
        h6 = self.resb6_3(hx)

        #-------------Bridge-------------
        hx = self.relubg_1(self.bnbg_1(self.convbg_1(h6)))  # 8
        hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx)))
        hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx)))

        # -------------Decoder-------------

        hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg, h6), 1))))
        hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx)))
        hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx)))

        hx = self.upscore2(hd6)  # 8 -> 16

        hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1))))
        hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx)))
        hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx)))

        hx = self.upscore2(hd5)  # 16 -> 32

        hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hx, h4), 1))))
        hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx)))
        hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx)))

        hx = self.upscore2(hd4)  # 32 -> 64

        hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3), 1))))
        hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx)))
        hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx)))

        hx = self.upscore2(hd3)  # 64 -> 128

        hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2), 1))))
        hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx)))
        hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx)))

        hx = self.upscore2(hd2)  # 128 -> 256

        hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1), 1))))
        hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx)))
        hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx)))

        # -------------Side Output-------------
        db = self.outconvb(hbg)
        db = self.upscore6(db)  # 8->256

        d6 = self.outconv6(hd6)
        d6 = self.upscore6(d6)  # 8->256

        d5 = self.outconv5(hd5)
        d5 = self.upscore5(d5)  # 16->256

        d4 = self.outconv4(hd4)
        d4 = self.upscore4(d4)  # 32->256

        d3 = self.outconv3(hd3)
        d3 = self.upscore3(d3)  # 64->256

        d2 = self.outconv2(hd2)
        d2 = self.upscore2(d2)  # 128->256

        d1 = self.outconv1(hd1)  # 256

        # -------------Refine Module-------------
        dout = self.refunet(d1)  # 256

        return torch.sigmoid(dout), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(
            d4), torch.sigmoid(d5), torch.sigmoid(
            d6), torch.sigmoid(db)
